To alleviate the computation cost of current NAS approaches, we propose to analyze the NAS problem from a network topology perspective. This idea is motivated by observing that the tediousness and complexity of current NAS approaches stem from the lack of understanding of what actually contributes to a neural network's accuracy. Indeed, the innovations on the topology of neural architecture, especially the introduction of skip connections, have achieved great success in many applications [1]}, [2]}. This is because, in general, the network topology (or structure) strongly influences the phenomena taking place over them [3]}. For instance, how closely the social network users are interconnected directly affects how fast the information propagates through the network [4]}.
Similarly, a DNN architecture can be seen as a network of connected neurons. As discussed in [5]}, the topology of deep networks has a significant impact on how effectively the gradients can propagate through the network and thus the test performance of neural networks. These observations motivate us to take an approach from network science to quantify the topological property of neural networks to accelerate NAS.
